Triple
T8553638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | English in Louisiana |
E202505
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Yat English |
E211
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Yat English | Statement: [English in Louisiana, hasDialect, Yat English]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yat English Context triple: [English in Louisiana, hasDialect, Yat English]
-
A.
English
chosen
English is a widely spoken West Germanic language that serves as a global lingua franca in education, business, science, and international communication.
-
B.
Eton language
Eton is a Bantu language of central Cameroon, closely related to Ewondo and spoken by the Eton people.
-
C.
Broken English
Broken English is a 1979 British comedy film starring Michael Caine and John Clive, known for its satirical take on language and communication.
-
D.
Tai Ya language
The Tai Ya language is a Southwestern Tai language spoken primarily by the Tai Ya people in parts of China and Southeast Asia.
-
E.
English (film)
English (film) is a cinematic work produced in the English language, likely featuring the character Rita Vrataski.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca832610e08190b3b6c6cd2c250255 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe8894e7c8190bc0ae2ceec473ecb |
completed | March 31, 2026, 3:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6dd67d288190a147562a99ecde56 |
completed | April 2, 2026, 1:23 p.m. |
Created at: March 30, 2026, 6:19 p.m.